In this paper, we address these challenges and propose a range-adaptive frame interpolation model, which can be applied to reduce the amounts of point cloud transmission (as shown in Fig. REF ). The proposed approach follows the successful paths of flow-based VFI methods with novel adaptions. Specifically, first, 3D point clouds are projected to a set of 2D range images as input frames. Then, we estimate the bi-directional optical flows from two input frames via our flow estimator, which is modified from PWC-Lite, a lightweight version of PWC-Net [1]}. Instead of the feature extractor adopted in the existing VFI methods for color images, we propose a range-adaptive feature extractor to obtain high-efficient representations of range images. Then, we utilize softmax splatting [2]} to warp the input frames and range features with the estimated flows. Finally, the in-between frame is generated. As shown in Fig. REF , our method can deliver a competitive performance for point cloud frame interpolation.
